SHORT PAPER
International J. of Recent Trends in Engineering and Technology, Vol. 3, No. 2, May 2010
Simulation of Power Electronic Wave Forms of
Single Phase Full Converter using
MATLAB/SIMULINK and ANN
Vipul Sharma1, S.S.Pattnaik 2, Agam Kumar Tyagi1, S. Devi2, Tanuj K. Garg1, N. K. Agarwal1
1
Gurukul Kangri Vishwavidyalaya, Haridwar, Uttarakhand
vipul.s@rediffmail.com
2
NITTTR, Chandigarh
shyampattnaik@yahoo.com
One difficulty in all the above estimation methods is
that the response tends to be slow because of the
processing involved. To avoid the complexity of
estimation, it may be possible to get the solution by oneor multidimensional look-up tables in microcomputer
memory. However, for improvement of accuracy, the size
of the look up table should be large, or interpolation
calculation is required. In recent years, several
researchers have incorporated artificial neural networks
in several adaptive schemes in a number of timeindependent or time dependent settings, either in primary
or supporting role, but the applications in the field of
power electronics are comparatively less. In this paper,
the simulation of a full wave converter is performed using
MATLAB/SIMULINK for different firing angles [6].
Single-phase thyristor ac converter current waveforms
have been taken into consideration and neural network
have been trained to estimate the cut off angle[7,8]. A
back propagation training algorithm was used for training
the network. A comparison of the two methods have been
done and analyzed.
Abstract—Power Electronic converters are widely being
used these days for various applications ranging from small
to large power. Full converters are widely been used for
conversion of single phase input to the desired output
waveform. This paper compares the performance of this
converter using different operating tools. The cutoff angle of
line current in a single phase thyristor controlled full
converter with RL load has been taken into consideration in
discontinuous current mode. ANN has been trained using
Back propagation method and the performance of this ANN
based estimator is compared with the simulation work done
in MATLAB. The results of these simulations are elaborated
in the paper discussing converter performance with
different loading conditions.
Index Terms— Controlled converters, Matlab, Waveform
Estimation, ANN, Power Electronics.
I.
INTRODUCTION
Single phase full converter is widely being used for
different supply requirement these days. Power electronic
circuits generate complex voltage and current waveforms
because of their switching mode operation. For control,
Monitoring, and diagnostic purposes, it is frequently
necessary to process these waveforms and generate the
outputs, such as rms current, active power, reactive
power, displacement factor etc. it is possible to make
estimation from a basic, closed –form mathematical
model of the system, if such a model can be obtained [1].
The model equations are often nonlinear, complex, and
distributed in nature, making this approach difficult [2].
The topological form of the system can be simulated on
the computer with the known parameters, and then
analytical calculation can be made on the resulting
waveforms [3,4,5]. Sometimes the mathematical model
parameter may be totally unknown, making these
estimation approaches impossible. For a prototype
operating system, electronic instrumentation (both
hardware and software) techniques are extensively used
for such measurements. For example the waveform may
be captured and then analyzed by Fast Fourier Transform
in real time to derive the estimated outputs. Similar
techniques can be used for estimation of the waveforms
recorded on oscilloscope or chart recorder.
II. NEURAL NETWORK PRINCIPLES
Neural networks or artificial neural networks (ANN) are
the interconnection of artificial neuron that tends to
emulate the human brain [7]. The model of an artificial
neuron that closely matches a biological neuron is given
by an op-amp, summer like configuration shown in fig. 1.
71
© 2010 ACEEE
DOI: 01.IJRTET.03.02.519
SHORT PAPER
International J. of Recent Trends in Engineering and Technology, Vol. 3, No. 2, May 2010
The input signals X1, X2, X3 …Xn are normally
continuous signals that flow through synaptic weights and
then accumulate in the summing node, as shown. The
weights can be negative or positive, and correspondingly,
amplify or alternate the respective signals coming to the
summing node. The summed signal then flows to the
output through a transfer function that is usually
nonlinear. The transfer function can be the threshold type,
signam type, or linear threshold type, or it can be
nonlinear continuously varying type, such as the sigmoid,
inverse tan, hyperbolic or Gaussian type. The sigmoid
transfer function and tan hyperbolic function are most
commonly used and are given by the equations:
( )=
0< ( )<1
means that if an input set of data corresponds to a definite
signal pattern, the network can be “trained” to give a
correspondingly desired pattern at the output.
(1)
( ) = tanh
−1< ( ) <1
(2)
Where ‘a’ is the gain that adjusts the slope of the
function. At high gain, approaches a step function. The
sigmoidal
function
is
nonlinear,
monotonic,
differentiable, and has the largest incremental gain at zero
signal, and these properties are of particular interest in the
application of neural networks. Nonlinearity of the
transfer function gives the network capability to emulate
nonlinear mapping properties. A neural network can be
classified as a feed forward or feedback type, depending
on the interconnection of the neurons. At present, by far
the majority of applications use feed forward multilayer
network that consists of three layers: the input layer, the
hidden layer, and the output layer. The circles represent
neurons, and a weight-adjustment feature is included. The
input and output layers (defined as buffers) have neurons
equal to the respective number of signals. For example, in
fig. 2, the input signals may be current waveform pattern
characterized by the firing angle (α) and load phase angle
(Φ), and the corresponding output signals may be total
average load current and cutoff angle (θ). The particular
network shown with five hidden layer neurons can be
defined as 2-5-2 network. The input layer neurons do not
have a transfer function, but there are scale factors to
normalize the input signals. Similarly, there also can be
scale factors at the output for demoralization. There can
be more than one hidden layer. The number of hidden
layers and the number of neurons in each layer depends
on the complexity of the problem being solved and the
desired accuracy. Note that the neural network computes
very fast in a parallel and a distributed manner compared
with slow sequential computation in a conventional Von
Neumann computer that uses a centralized CPU and a
central memory. Besides, the neural network has faulttolerant properties and provides noise-immune
computation [7,8]. If a few weights are erroneous or
several connections are destroyed in a large network, the
output remains practically unaffected because of
distributed knowledge throughout the network. The
computation of the neural network basically relates to
nonlinear mapping or a pattern recognition function. This
The network has the capability to “learn” because of the
distributed intelligence or “associative memory” property
contributed by the weights. The input-output pattern
matching is possible if the network is trained, i.e., if
appropriate weights are selected. With the network
initially untrained, i.e., with the weights selected at
random, the output signal pattern will totally mismatch
the desired pattern for a given input pattern. The actual
output pattern can be compared with the desired output
pattern, and the weights can be adjusted by an algorithm
until pattern matching occurs. i.e., the error becomes
small. The back propagation training algorithm is most
commonly used for feed forward neural networks. The
training is usually automated with an off-line computer
simulation program that uses a large number of inputoutput example patterns. The example patterns can be
derived from analysis, simulation or by experiment if the
model is totally unknown. At completion of the training,
the weights are downloaded to the prototype network. A
trained network should be able not only to recall all the
example input-output patterns (look-up table function)
but also to interpolate the example patterns.
III. ESTIMATION FOR 1-Φ THYRISTOR
CONVERTER CURRENT WAVE FORMS
It is not an absolute requirement that converters use
large energy storage elements. If small storage elements
are used, a converter can exhibit extra configurations,
such as “all off” or “all on” switch behavior. The
concepts of critical inductance and critical capacitance
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DOI: 01.IJRTET.03.02.519
SHORT PAPER
International J. of Recent Trends in Engineering and Technology, Vol. 3, No. 2, May 2010
provide a convenient way to determine whether storage
components are small in the sense of power electronics.
The critical inductance for a converter, Lcrit, is defined as
the smallest inductance for which iL>0 under all allowed
conditions in a power electronic circuit [9,10,11]. The
value of Lcrit is extremely useful. It defines the boundary
between normal and discontinuous modes, and can be
used as the basis for inductor ripple design.
In AC to DC converters the switching frequency is
usually low and the value Lcrit tends to be relatively high.
Many industrial converters operate with subcritical
inductance because of the low frequency. When L<Lcrit
most converters exhibit output voltages higher than when
L≥ Lcrit. In commercial systems this behavior is
compensated through the use of larger phase delay angles
and automatic control. AC converters actually require
subcritical inductance for proper operation. If the current
does not return to zero during half cycle, commutation
failure can occur. Commutation failure defines a situation
in which the terminal conditions on a switch are
inconsistent with attempts to control the switch. If an
SCR is reverse biased when a gate pulse arrives, the
device will not turn on, and a commutation failure has
occurred. Commutation failure in AC converters can
prodoce DC average output values. This can be a serious
problem and AC regulators are normally intended for
resistive loads to avoid trouble.
Fig.4a. shows the waveforms for a single-phase bridge
thyristor converter of circuit shown in Fig.3. Fig. 4b
shows the Matlab/Simulink waveform output of the
circuit. Under discontinuous conduction with resistance-
Fig. 3 shows the simple circuit of a single-phase thyristor
AC converter with R-L-E load. The firing angle of the
thyristor can be controlled symmetrically to control the
power to the load. The result obtained for this converter is
shown in fig. 3a. The DC load current waveform in the
thyristor AC converter is nonsinusoidal and depends on
firing angle and the impedance angle [12,13].
Feedforward neural networks are trained to estimate the
cut-off angle [14,15,16,17].
inductance-CEMF load. The instantaneous load current in
Fig. 4 can be expressed as[1]
Id =
(3)
Where
.
α-
firing angle
Vs – rms value of supply voltage
R-
Load resistance
Z- R+jωL impedance
ωϕ-
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DOI: 01.IJRTET.03.02.519
Angular frequency
Impedance angle (tan-1(ωL/R))
SHORT PAPER
International J. of Recent Trends in Engineering and Technology, Vol. 3, No. 2, May 2010
Assuming the supply voltage and frequency sinusoidal
and constant (i.e. 220 volts, 50 Hz AC), the equation.(4)
shows that the DC load current is a function of firing
angle (α), and impedance angle (ϕ). To train a network
where all these input variables changes [18,19,20]. A
three layer1-30-1 network is used. The training data for
simulation can be summarized as follows:
Input
Range of α: 0-1800 in 20 steps (100 intervals,
except extra step at 50 and 1750) under limiting
conditions.

Values of ϕ: tan-1(2), tan-1(1), tan-1(.5).

Values of m: 0.2, 0.4,0.8
Output
Cut-off angle (θ) calculated by MATLAB fig. 3a
and estimated by a C++ neuron model.
IV. RESULTS
Simulation of the circuit shown in figure 3 is done using
MATLAB/SIMULINK for different values of firing
angles and θ in discontinuous conduction mode. The
results obtained are plotted in figure 6. Firstly we kept θ
constant and then obtained output for different values of
the firing angle. After that the firing angles of the
thyristors are kept constant and then the output is
obtained for different values of θ. These calculated values
were then used to train the artificial neural networks to
estimate the Cutoff angle θ. We used a Neural Model
Developed in C++ as Artificial Neural Networks. A very
large number of training steps were used to train the
complex network and the error was found to converge to
less than 0.1%.
Figure 4b. Showing output of 1phase Thyristor converter
with RLE Load.
Equation.3 is valid in the range α≤ ωt ≤ θ for
discontinuous conduction. From the current waveform, Id
=0 at ωt= θ. It yields
e – (R/ ωL) (θ – α) = {cos (Φ) sin (θ–Φ) –m}/ {cos (Φ) sin (α–
Φ)–m}
(4)
Fig.6. Variation of cut-off angle θ with firing angle α
This is a transcendental equation relating parameters
α, m (Vc/Vs), θ, and ωL/R (i.e. Φ).
he DC load current is given by
Id=√2 Vs [cos (α)-cos (θ)-m (θ-α)]/ (пR)
Fig.7 shows the estimator performance Cutoff angle θ.
The results are verified with the simulation results
obtained. The simulations were also performed for RL &
RLC loads on the same converter and an idea of the
(5)
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SHORT PAPER
International J. of Recent Trends in Engineering and Technology, Vol. 3, No. 2, May 2010
ACKNOWLEDGEMENT
converter for different loading conditions is also
observed. These observations will utilized for further
work continuing in this area.
The authors are thankful to the reviewer for his/her
valuable suggestions.
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Fig.7 Results obtained from Artificial Neural Network
V. CONCLUSION
The Paper successfully demonstrates the validity of
feed- forward neural networks for the estimation of the
power electronics waveforms. The application of artificial
neural networks for the estimation of cut-off angle seems
to be a simple, inexpensive, and accurate method having
very good agreement with the experimental results. The
agreement with the derived results without using any
practical power electronics circuit for training makes it
possible to realize a universal artificial network to
evaluate the cut-off angle with accuracy. The presented
method of using ANN for estimation of power electronics
waveforms is one of the simplest and accurate methods.
The inputs of the artificial neural networks have been
carefully chosen. This gives the option of using the
network for testing the waveforms for other power
electronics circuitry made out of changing any of the
parameters used as input of the networks. The ANN’s
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The simulations were also performed for RL & RLC
loads on the same converter and an idea of the converter
for different loading conditions is also observed. These
observations will utilized for further work continuing in
this area.
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